GraphSeg: Segmented 3D Representations via Graph Edge Addition and Contraction

• URL: http://arxiv.org/abs/2504.03129v1
• Date: Fri, 04 Apr 2025 02:42:45 GMT
• Title: GraphSeg: Segmented 3D Representations via Graph Edge Addition and Contraction
• Authors: Haozhan Tang, Tianyi Zhang, Oliver Kroemer, Matthew Johnson-Roberson, Weiming Zhi,
• Abstract summary: We present GraphSeg, a framework for generating consistent 3D object segmentations from a sparse set of 2D images.<n>We show that GraphSeg achieves robust segmentation with significantly fewer images and greater accuracy than prior methods.
• Score: 23.79427101656399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robots operating in unstructured environments often require accurate and consistent object-level representations. This typically requires segmenting individual objects from the robot's surroundings. While recent large models such as Segment Anything (SAM) offer strong performance in 2D image segmentation. These advances do not translate directly to performance in the physical 3D world, where they often over-segment objects and fail to produce consistent mask correspondences across views. In this paper, we present GraphSeg, a framework for generating consistent 3D object segmentations from a sparse set of 2D images of the environment without any depth information. GraphSeg adds edges to graphs and constructs dual correspondence graphs: one from 2D pixel-level similarities and one from inferred 3D structure. We formulate segmentation as a problem of edge addition, then subsequent graph contraction, which merges multiple 2D masks into unified object-level segmentations. We can then leverage \emph{3D foundation models} to produce segmented 3D representations. GraphSeg achieves robust segmentation with significantly fewer images and greater accuracy than prior methods. We demonstrate state-of-the-art performance on tabletop scenes and show that GraphSeg enables improved performance on downstream robotic manipulation tasks. Code available at https://github.com/tomtang502/graphseg.git.

Related papers

• WildSeg3D: Segment Any 3D Objects in the Wild from 2D Images [16.107027445270887]
  We introduce WildSeg3D, an efficient approach that enables the segmentation of arbitrary 3D objects across diverse environments.<n>A key challenge of this feed-forward approach lies in the accumulation of 3D alignment errors across multiple 2D views.<n>We also propose Dynamic Global Aligning (DGA) and Multi-view Group Mapping (MGM) for real-time interactive segmentation.
  arXiv  Detail & Related papers  (2025-03-11T13:10:41Z)
• DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting [0.0]
  We present a decoupled 3D segmentation pipeline to ensure modularity and adaptability to novel 3D representations. We evaluate on synthetic and real-world indoor datasets, demonstrating improved performance over comparable NeRF-based pipelines.
  arXiv  Detail & Related papers  (2024-12-14T21:26:44Z)
• 3D Part Segmentation via Geometric Aggregation of 2D Visual Features [57.20161517451834]
  Supervised 3D part segmentation models are tailored for a fixed set of objects and parts, limiting their transferability to open-set, real-world scenarios. Recent works have explored vision-language models (VLMs) as a promising alternative, using multi-view rendering and textual prompting to identify object parts. To address these limitations, we propose COPS, a COmprehensive model for Parts that blends semantics extracted from visual concepts and 3D geometry to effectively identify object parts.
  arXiv  Detail & Related papers  (2024-12-05T15:27:58Z)
• View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fields [52.08335264414515]
  We learn a novel feature field within a Neural Radiance Field (NeRF) representing a 3D scene. Our method takes view-inconsistent multi-granularity 2D segmentations as input and produces a hierarchy of 3D-consistent segmentations as output. We evaluate our method and several baselines on synthetic datasets with multi-view images and multi-granular segmentation, showcasing improved accuracy and viewpoint-consistency.
  arXiv  Detail & Related papers  (2024-05-30T04:14:58Z)
• SAM-guided Graph Cut for 3D Instance Segmentation [60.75119991853605]
  This paper addresses the challenge of 3D instance segmentation by simultaneously leveraging 3D geometric and multi-view image information. We introduce a novel 3D-to-2D query framework to effectively exploit 2D segmentation models for 3D instance segmentation. Our method achieves robust segmentation performance and can generalize across different types of scenes.
  arXiv  Detail & Related papers  (2023-12-13T18:59:58Z)
• Segment Any 3D Gaussians [85.93694310363325]
  This paper presents SAGA, a highly efficient 3D promptable segmentation method based on 3D Gaussian Splatting (3D-GS) Given 2D visual prompts as input, SAGA can segment the corresponding 3D target represented by 3D Gaussians within 4 ms. We show that SAGA achieves real-time multi-granularity segmentation with quality comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-01T17:15:24Z)
• MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation [91.6658845016214]
  We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. We render a 3D shape from multiple views, and set up a dense correspondence learning task within the contrastive learning framework. As a result, the learned 2D representations are view-invariant and geometrically consistent.
  arXiv  Detail & Related papers  (2022-08-18T00:48:15Z)
• SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences [76.28527350263012]
  We propose a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. We aggregate PointNet features from primitive scene components by means of a graph neural network. Our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35 Hz.
  arXiv  Detail & Related papers  (2021-03-27T13:00:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.